Have you ever looked at a codebase you wrote years ago, and your only thought is: “What was I thinking?” Back in 2020, I was diving head-first into Kubernetes and the cloud-native world to build a notification system. I was so excited to finally be able to deploy my app to the cloud, and I was determined to make it the best it could be.
If you’ve spent any time in the Go ecosystem, you’ve seen the memes. The “wall of if err != nil” is the most common critique of the language. To developers coming from Java, Python, or TypeScript, Go’s error handling feels like a step backward, a return to the days of manual checks and boilerplate.
Many of us have been there. You’re whiteboarding a new system. It needs to be containerized, it needs to scale, and it needs to be resilient. The hand reaches for the marker and draws the familiar hexagon: Kubernetes. It’s the industry standard. It’s what the “big players” do. But I believe infrastructure should be a response to a requirement, not a default setting.
When we’re hit with high-volume data in Go, we usually reach for the standard worker pool. It’s reliable, it’s fast, and it works—until the order of execution actually matters. The moment “order” is mentioned, I see a lot of teams start over-engineering their cloud setup. They begin partitioning queues at the infrastructure level, spinning up dedicated consumer pools for every partition, and adding massive complexity to their monitoring and deployment pipelines.
Many internal projects start with quickly built APIs that can become difficult to manage over time. This article provides practical advice on refactoring these “prototype” APIs, transforming them into stable and efficient solutions suitable for production environments.